JP5049246B2 - Object shape evaluation device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a device for evaluating the shape of an object in which the congruence transformation between a measurement point group and a reference point group is properly performed, even when the measurement error of a specific area is large, due to the conditions on measurement techniques of a measurement device, and as a result, the object shape evaluation of the measurement object is performed properly. <P>SOLUTION: The device of evaluating the shape of the object aligns the measurement points and the reference points, on the basis of sequential convergence processing for sequential convergence of the distance between many measurement points corresponding to the shape of the measurement object and many reference points corresponding to the reference shape of the measurement object, and evaluates the shape of the measurement object, on the basis of the measurement point data and the reference point data after the alignment. In the alignment processing, an inter-adjacent-point distance weight coefficient is determined, on the basis of the inter-adjacent-point distance between adjacent measurement points, or the inter-adjacent-point distance between the adjacent reference points, and the inter-adjacent-point distance weight coefficient is used, when a sequential convergence evaluation value in the sequential convergence processing is to be obtained. <P>COPYRIGHT: (C)2010,JPO&amp;INPIT

Description

  The present invention relates to an object shape evaluation apparatus that evaluates the shape of a measurement object by aligning a large number of measurement points corresponding to the shape of the measurement object and a large number of reference points corresponding to the measurement points.

  As a conventional technique for evaluating the measurement result for a measurement object by aligning (matching) the measurement point group and the reference point group, the position of the object to be measured is combined with stereo vision and an ICP (Iterative Closest Point) algorithm. An image measurement method that accurately measures the posture is known (for example, see Patent Document 1). In this stereo image measurement method, a measurement point cloud is obtained by stereo-processing a plurality of camera images of the three-dimensional shape of the measurement target, and is visible from the known shape information of the measurement target and the relative position and orientation of the stereo camera. The model point cloud is determined according to the spatial density of the measurement point group using only the predicted shape information of the visible part, and matching is performed between the measurement point group and the model point group using an ICP algorithm. The position / orientation corresponding to the model group having the smallest evaluation function is adopted as the measurement result.

  However, the ICP algorithm is a technique for positioning or merging position data that does not include an exceptional value in principle (problem for obtaining the most congruent congruent transformation of both data). Therefore, if there is a non-overlapping part or an additional part of data included in the distance data set to be positioned, there arises a problem that these function as exception values and hinder convergence to the correct state.

  In order to solve such problems of the ICP algorithm, an ICP positioning method in which M estimation is introduced has been proposed (see, for example, Non-Patent Document 1). In this M-estimated ICP positioning method, when the congruent transformation is successively converged while updating the correspondence between two point groups (for example, a measurement point group and a reference point group), a residual amount (for example, a measurement point and a reference point) The weight is set according to the distance. By changing the evaluation value in positioning for each corresponding point by this weight, it is possible to suppress the adverse effect due to the exceptional value. However, when measuring the three-dimensional shape of the measurement object, the measurement error increases for the inclined surface of the measurement object due to the measurement technical conditions of the measurement device, and the measurement point and the reference point Only by weighting based on the distance, it is difficult to properly and sequentially converge the joint transformation between the measurement point group and the reference point group.

Japanese Patent Laying-Open No. 2008-14691 (paragraph number 0006, FIG. 1) Shunichi Kaneko et al. “Robust ICP positioning method with M estimation”, Journal of Precision Engineering V0l.67, No.8, 2001, 1276-1280

  In view of the above situation, the object of the present invention is that the joint conversion between the measurement point group and the reference point group is appropriate even when the measurement error becomes large in a specific measurement region due to the measurement technical conditions of the measurement apparatus. As a result, an object shape evaluation apparatus is provided in which the object shape evaluation of the measurement object is appropriately performed.

  In order to achieve the above object, a feature configuration of the object shape evaluation apparatus according to the present invention includes a measurement data input unit that inputs measurement point data including position information of a large number of measurement points corresponding to the shape of the measurement object, A reference data storage unit that stores reference point data including position information of a large number of reference points corresponding to the reference shape of the measurement object, and a sequential convergence process that sequentially converges the distance between the corresponding measurement point and the reference point An alignment processing means for aligning the measurement point and the reference point based on the shape, and a shape of the measurement object based on the measurement point data and the reference point data after the alignment processing by the alignment processing means Shape evaluation means for evaluating the distance between adjacent points based on a distance between adjacent points between adjacent measurement points or a distance between adjacent points between adjacent reference points. It has a weighting coefficient determination means for determining a look coefficients is to use the distance weight factor between the adjacent points when determining the sequential convergence evaluation value in the sequential convergence process.

  According to this feature configuration, the distance between adjacent points that is the distance between each point belonging to the point group (measurement point or reference point) in either the measurement point group or the reference point group. A weighting factor is assigned based on The assigned weighting factor is used to determine the sequential convergence evaluation value in the sequential convergence process that sequentially converges the distance between the corresponding measurement point and the reference point, and the effect of the distance between adjacent points on the sequential convergence evaluation value The degree is adjusted. For example, there is a slight measurement point deviation at a measurement point where the distance between adjacent points is larger than the other points, or at a measurement point corresponding to a reference point where the distance between adjacent points is larger than the others. There may be a large difference in the position coordinate value between the measurement point and the reference point. This is because, when the measurement surface by a laser beam or the like is curved or inclined at an angle close to a right angle with respect to the measurement reference surface, a slight deviation of the measurement scanning pitch leads to a large deviation of the measurement point, This is because the positional correspondence between the reference point and the measurement point is deteriorated. In such a measurement situation, a smaller weighting factor is assigned to a point having a large distance between adjacent points, and the influence of the point on the convergence evaluation value is sequentially suppressed, so that the measurement point group and the reference point group Appropriate congruent conversion between can be realized. As a result, the object shape of the measurement object is properly evaluated.

  Further, as a further characteristic configuration in the object shape evaluation apparatus according to the present invention, the weighting factor determination means determines a distance weighting factor between corresponding points based on a distance between corresponding points between the corresponding measurement point and a reference point. It is also preferable to use the distance weight coefficient between corresponding points when the alignment processing means obtains the sequential convergence evaluation value in the sequential convergence processing. This feature is a robust ICP (Iterative Closest Point) positioning method known as a so-called M estimation method, which has been described in detail in Non-Patent Document 1 and will not be described in detail here. A distance weight factor between corresponding points determined based on the distance between corresponding points between the reference point and the reference point is used when obtaining a sequential convergence evaluation value in the sequential convergence process, and a measurement point having a large distance between corresponding points ( The degree of influence on the successive convergence evaluation value is adjusted by so-called outliers. Thereby, in this preferable object shape evaluation apparatus, the distance weight between corresponding points determined based on the distance between corresponding points between the corresponding measurement point and the reference point, and the distance between adjacent points in the same point group. With the distance weight coefficient between adjacent points determined based on the measurement point data estimated to introduce an error, the influence of the measurement point data can be eliminated or reduced. As a result, the object shape evaluation of the measurement object is performed more appropriately.

  In determining the distance weight between adjacent points based on the distance between adjacent points by the weight coefficient determining means, either the distance between adjacent points in the measurement point group or the distance between adjacent points in the reference point group may be adopted. When the distance between adjacent points in the reference point group is adopted, the distance weight coefficient between adjacent points can be obtained in advance before measurement. When there are a plurality of measurement objects of the same shape, it is effective because the distance weight coefficient between adjacent points obtained from common reference point data (reference point group) can be used in each object shape evaluation. At that time, it is more preferable that the distance weight coefficient between adjacent points obtained in advance is calculated in advance and tabulated in the apparatus.

  Since the distance weighting factor between adjacent points suppresses the influence of the point (measurement point or reference point) having a distance between adjacent points that is too wide on the convergence evaluation value, the distance weighting factor between adjacent points determines the distance between adjacent points. It is effective only to be obtained by using a threshold function as a parameter, and the calculation for obtaining the distance weight coefficient between adjacent points is thereby simplified. In this case, a threshold function having “0 or a number close to 0” and “1” as values with one threshold as a boundary is preferable.

  The object shape evaluation apparatus according to the present invention assigns a smaller weight coefficient to a point having a large distance between adjacent points, thereby suppressing the influence of the point on the successive convergence evaluation value. Therefore, a slight shift in the measurement scanning pitch leads to a large shift in the measurement point, which is particularly suitable for the object shape evaluation of the measurement system in which the position correspondence between the reference point and the measurement point is deteriorated. Therefore, when the measurement point data of the object shape evaluation apparatus according to the present invention is three-dimensional position data extracted from a distance image obtained by processing a captured image of the measurement object irradiated with slit light. Suitable for.

  First, the calculation principle of the weighting coefficient used for the successive convergence evaluation in the successive convergence processing employed in the object shape evaluation apparatus according to the present invention for aligning the reference point group and the measurement point group will be described. Here, a method based on the ICP algorithm is adopted as the sequential convergence process. In this ICP algorithm, as schematically shown in FIG. 1, each point of the reference point group (reference point data) is a point corresponding to the closest measurement point group (measurement point data). The congruent transformation parameter that minimizes the sum of squares of the distance is estimated and converged sequentially. In the alignment by such ICP algorithm, in the case of shape measurement in which measurement points are determined at an equal measurement scan pitch as shown in FIG. 1, a slight scan pitch shift may occur depending on the orientation of the measurement surface. This leads to a large displacement of the measurement points (in FIG. 1, measurement points S4, S5, S6 apply to this). Therefore, it is not preferable to use a measurement point at which an error between an assumed position coordinate and an actual position coordinate is expected to be large and a reference point corresponding thereto as it is for the successive convergence evaluation in the successive convergence process. In order to avoid such problems, the improved ICP algorithm applied in the present invention incorporates weighting factors as described below.

  In FIG. 1, a reference point group (reference point data): a reference point included in M is indicated by m, and a subscript (natural number) is given corresponding to the measurement order. Measurement point group (measurement point data): The measurement point included in S is indicated by s, and a subscript (natural number) is given corresponding to the measurement order in the same manner as the reference point. In the sequential convergence process using the conventional ICP algorithm for positioning the measurement point group over the reference point group, the closest reference point in the reference point group is set as the corresponding point for each measurement point in the measurement point group. A joint transformation parameter (R, t) that minimizes the sum of squares of the distances between corresponding points is obtained. Here, R is a rotation matrix and t is a translation vector. At that time, among the corresponding combinations of reference points and measurement points, if the combination of the above-mentioned measurement points where a large error is expected is handled in the same way as other combinations, there is a possibility that it will be affected by errors that cannot be ignored. is there. Therefore, the distance weight coefficient between adjacent points is assigned based on the distance between adjacent points of the measurement point or the reference point, and the adverse effect is suppressed. Here, the weighting coefficient for the distance between the corresponding points is calculated based on the distance between the adjacent points between the reference points. Of course, the distance between the corresponding points is determined based on the distance between the adjacent points between the measurement points. A weighting factor for the distance may be calculated.

Three-dimensional coordinates _{(x i, y i, z} i) the reference point having: m _i between adjacent point distance: d _i is calculated by the Pythagorean theorem, the square is
d _i ² = x _i ² + y _i ² + z _i ²
The distance weight coefficient between adjacent points: γ _i is the weight function Γ,
It is obtained by γ _i = Γ (d _i ² ).
For example, it is convenient to set the weight function: Γ to the following threshold function.
When d _i ² is a threshold value: d _th or more, γ _i = 0.01
When d _i ² is less than the threshold: d _th γ _i = 1
The threshold value: d _th is determined appropriately according to the characteristics of the reference point group and the measurement point group, so that the measurement point expected to have a large error between the assumed position coordinate and the actual position coordinate and the corresponding reference point Can suppress adverse effects on the successive convergence evaluation.

It should be noted that it is useful to use M estimation as disclosed in the above-mentioned non-patent document in the alignment based on such an ICP algorithm. The ICP algorithm in which M estimation is introduced will be briefly described with reference to FIG. The introduction of M estimation is to use the distance weight factor between corresponding points determined based on the distance between corresponding points between the corresponding measurement point and the reference point for the successive convergence evaluation in the successive convergence process. For example, assuming that the distance between corresponding points is e _i , the distance weight coefficient between corresponding points: ρ _i is
ρ _i = Ρ (e _i ) Again, the weight function: Ρ can be a threshold function as follows:
When | e _i | is less than the set width B _i ,
^{_{ρ i = (B i 2/}} 2) (1- (1- (e i / B i) 2)
When | e _i | exceeds the set width B _{i
^{ρ i = (B i 2/}} 2).

When the distance weight coefficient between adjacent points: γ _i and the distance weight coefficient between corresponding points: ρ _i are also used for successive convergence evaluation, the total weight coefficient: w is the distance weight coefficient between adjacent points: γ _i and the corresponding points. It can be derived from a function having a distance weight coefficient: ρ _i as a parameter. Thus, sequential convergence process sequential convergence evaluation value in: J, if the number of corresponding points is N, between the corresponding point distance: e _i and between the corresponding point distance weighting factor: [rho _i with the neighboring point distance weight factor: gamma An evaluation function with _i as a parameter can be derived using H.
J = (1 / N) ΣH (e _i , ρ _i , γ _i )
In order to simplify the calculation, if the total weight coefficient: w _i is multiplied by each weight coefficient,
J = (1 / N) ΣH (e _i , w _i ), w _i = ρ _i × γ _i
It becomes.

Next, an algorithm for efficiently determining a specific surface defect such as turning up from the reference point group (reference point data) and the measurement point group (measurement point data) aligned using the above-described algorithm is illustrated. This will be described with reference to FIG.
First, a corresponding point distance threshold (first threshold): TA corresponding to a distance between corresponding points: ei set in advance is extracted as a miscorresponding region. In FIG. 2, measurement points S4, S5, S6, and S7 are extracted. Further, the distribution density of the measurement points belonging to the erroneous correspondence area is calculated. In order to easily calculate the distribution density, for example, the distance between adjacent points of the measurement points: di can be used. In other words, adjacent point distance: di is between adjacent points is preset distance threshold (second threshold value) is regarded as the near measurement point the measurement point is less than TL. As a result, corresponding points (neighboring measurement points) having a distribution density greater than or equal to a predetermined value are extracted. Further, it is checked whether or not the number of neighboring measurement points is equal to or greater than a preset neighboring point threshold (third threshold): TC. When the number of neighboring measurement points is larger than the neighboring point count threshold, a region defined by this neighboring measurement point group, that is, a surface region including this neighboring measurement point group is determined as a surface defect. As a result of surface shape measurement using a laser beam or the like for a surface defect area where the surface layer is peeled off, such as a rolled-up defect, this surface defect determination algorithm is “the corresponding point distance of the measurement point group becomes larger than the normal area ”And“ the measurement point group exists as a dense point group ”.

  An example of a surface defect evaluation apparatus that employs the alignment algorithm between the reference point group and the measurement point group and the surface defect determination algorithm as described above will be described. This surface defect evaluation apparatus is configured as a surface inspection apparatus that inspects a groove section of a measurement object in which a large number of straight deep grooves are aligned on the surface. FIG. 3 is a perspective view schematically showing the configuration of such a surface inspection apparatus.

  The surface inspection apparatus includes a measurement apparatus unit 1 and a controller 100 that performs control on the measurement apparatus unit 1 and evaluates the measurement result. The measurement apparatus unit 1 includes, as main components of the measurement system, a laser-type slit light source unit 2 that generates slit light, and an imaging unit 3 that captures an area of the measurement object irradiated with the slit light. ing. The slit light source unit 2 and the imaging unit 3 are integrally assembled as a measurement head MH. In addition, the measuring apparatus unit 1 supports the base 10, the portal frame 11 erected on the base 10, and the central portion of the portal frame 11 so that the measuring head MH can be moved up and down as main components of the mechanical system. A lifting mechanism 12 is provided. Furthermore, as a positioning mechanism for the measurement object, the rotation table 13 that rotates while placing the measurement object, and the XY for moving the rotation table 13 on the XY plane (a plane orthogonal to the slit optical axis). An X stage 14 that can move in the X direction and a Y stage 15 that can move in the Y direction are provided.

  The controller 100 is substantially formed as a computer unit, and includes a light source control unit 80, an image memory 81, an image processing unit 82, a three-dimensional measurement data calculation unit 83, an evaluation module 90 particularly related to the present invention, and a lifting mechanism. A control unit 84, a rotary table control unit 85, an X stage control unit 86, and a Y stage control unit 87 are provided. The rotary table control unit 85, the X stage control unit 86, and the Y stage control unit 87 control the operations of the rotary table 13, the X stage 14, and the Y stage 15, respectively, so that the measurement object is measured on the measurement plane (XY plane). Set to the appropriate measurement position. The elevating mechanism control unit 84 controls the operation of the elevating mechanism 12 and sets the height of the measuring head MH to the measurement object to a measurable height.

  As shown in FIG. 4, the slit light source unit 2 includes a laser slit projector 20 as a slit light source, and a cylindrical lens 21 that converts the slit light emitted from the laser slit projector 20 into parallel light parallel to the optical axis. Yes. By the cylindrical lens 21, the slit light that spreads out in a fan shape from the laser slit projector 20 is converted into parallel light parallel to the slit optical axis, and irradiates the measurement object.

  The imaging unit 3 includes a telecentric lens unit 30, an imaging unit 31 made up of a large number of light receiving elements (CCD and CMOS) arranged in a plane, and a P polarizing plate 32 arranged on the subject side of the telecentric lens unit 30. I have. The slit light from the slit light source unit 2 is irradiated on the surface of the measurement object, and the reflected light reflected there passes through the P polarizing plate 32 and the telecentric lens unit 30 along the imaging optical axis of the imaging unit 3. FIG. 5 shows how the image pickup unit 31 is reached. In this embodiment, an extremely narrow angle of about 11 degrees is adopted as the crossing angle at which the slit optical axis of the slit light source unit 2 and the imaging optical axis of the imaging unit 3 intersect, that is, the imaging angle α. Therefore, as shown by a dotted line in FIG. 5, when the imaging surface 31 a of the imaging unit 31 is in a posture perpendicular to the imaging optical axis, the range of the measurement depth along the slit optical axis direction is the object of the telecentric lens unit 30. If the depth of field is exceeded, an out-of-focus area occurs in the measurement depth range. In order to avoid this, the imaging surface 31a of the imaging unit 31 is tilted with respect to the imaging optical axis so as to create the tilt angle β, and the depth of field is gained by the principle of tilt shooting. As a result, a photographed image without blur can be acquired even in a measurement range that is greater than the depth of field of the telecentric lens unit 30.

  The captured image (image data) sent from the imaging unit 3 to the controller 100 is developed in the image memory 81. Furthermore, if necessary, the image processing unit 82 performs image processing such as coordinate conversion, level correction, and edge detection, and the light cutting line S by the slit light is detected. Since the three-dimensional measurement data calculation unit 83 knows the irradiation point and irradiation angle of the slit light, and the angle formed between the slit optical axis and the imaging optical axis, the three-dimensional measurement data calculation unit 83 determines the triangle from the coordinate value of the light cutting line S detected by the image processing unit 82. By calculating based on the surveying method, it is possible to obtain a large number of measurement point data (distance images) corresponding to the optical cutting line S, that is, the three-dimensional cross-sectional shape of the measurement object forming a plurality of linear deep grooves. . The distance image here is measurement data in which a three-dimensional position coordinate value is assigned to a pixel as a measurement point. Instead of the calculation based on the triangulation method, a method using a table storing the calculation result may be adopted.

  The measurement point data generated by the three-dimensional measurement data calculation unit 83 is transferred to the evaluation module 90. As shown in FIG. 6, the evaluation module 90 includes a surface evaluation module 90A and a defect evaluation module 90B. The surface evaluation module 90A applies the above-described alignment algorithm to the transferred measurement point data, aligns the measurement point group with the reference point group, and evaluates the surface shape of the measurement object. The defect evaluation module 90B evaluates the surface defect of the measurement object based on the alignment result of the measurement point group output from the surface evaluation module 90A to the reference point group. The surface evaluation module 90 </ b> A and the defect evaluation module 90 </ b> B perform each evaluation in units of predetermined blocks that are divided in advance in the measurement object.

The surface evaluation module 90A includes a measurement data input unit 91, a reference point data storage unit 92, a point group association unit 93, a weight calculation unit 94, a convergence evaluation unit 95, and a point group conversion unit 96. The measurement data input unit 91 receives measurement point data from the three-dimensional measurement data calculation unit 83. The reference data storage unit 92 stores reference point data indicating the surface shape of the measurement object. The reference point data is data indicating an ideal finished shape set so as to correspond to the measurement point for each predetermined block previously divided in the measurement object. The point group associating unit 93 associates the measurement point with the reference point based on the above-described alignment algorithm in the predetermined block unit. The weight calculation unit 94 calculates the total weight coefficient w described above, that is, the distance weight coefficient between adjacent points: γ _i and the distance weight coefficient between corresponding points: ρ _i , and obtains a value obtained by multiplying them. The point group conversion unit 96 obtains a joint conversion parameter for aligning the measurement point group with the associated reference point group, and converts the position coordinates of the measurement point group using the joint conversion parameter. The convergence evaluation unit 95 calculates the successive convergence evaluation value when the measurement point group tries to converge and move to the reference point group using the joint transformation parameter, and the movement of the measurement point group converges sequentially to the reference point group. Evaluate whether or not In this embodiment, the point group association unit 93, the weight calculation unit 94, the convergence evaluation unit 95, and the point group conversion unit 96 perform a sequential convergence process for sequentially converging the distance between the corresponding measurement point and the reference point. Based on this, an alignment processing means for aligning the measurement point and the reference point is configured. In particular, the weight calculation unit 94 determines two weighting factors, a distance weighting factor between adjacent points and a distance weighting factor between corresponding points. It is comprised as a weighting coefficient determination means to do.

  The surface defect evaluation module 90 </ b> B that functions as a surface defect evaluation unit includes an erroneous correspondence measurement group extraction unit 97 and a defect determination unit 98. Based on the surface defect determination algorithm described above, the erroneous correspondence measurement group extraction unit 97 regards a measurement point in which the distance between the corresponding measurement points and the reference point is greater than the first threshold as an erroneous correspondence measurement point. As a result, an erroneous correspondence measurement group (incorrect correspondence region) is extracted. When the number of neighboring measurement points whose distance from the adjacent measurement points among the erroneous measurement points included in the extracted erroneous measurement point group is smaller than the second threshold is larger than the third threshold, the defect determination unit 98 A region including the measurement point is determined as a surface defect.

  In this evaluation module 90, since the error in the laser slit light diffusion direction, here the Y direction, can be ignored, in the application of the algorithm described with reference to FIG. It can be executed considering only the value and the Z coordinate value.

  The procedure for inspecting the measurement object using the surface inspection apparatus configured as described above will be described below with reference to the flowchart shown in FIG. The object to be measured here is a rectangular plate having a large number of straight deep grooves formed on the surface thereof, and its measurement area is about 400 mm × 300 mm. This measurement area is divided into 100 mm × 15 mm measurement blocks. Four measurement blocks are scanned in one X-axis direction scan, and measurement point data representing the three-dimensional cross-sectional shape position of the linear deep groove in the measurement unit defined by the scanning pitch and imaging resolution is acquired and measured. Divide into blocks and store in memory. Each time one X-axis direction scan is completed, the Y-axis direction is moved at a predetermined pitch, and the X-axis direction scan for the next measurement block is performed in the reverse direction. By repeating such scanning in the X-axis direction and movement in the Y-axis direction, the measurement point data of the linear deep groove in the entire measurement region is acquired. Further, taking into account the generation of the measurement blind spot, the measurement in the same measurement region is performed again with the measurement object rotated by 90 degrees. In addition, the evaluation of the measurement result of the measurement object using the acquired measurement point data, that is, the inspection of the measurement object is performed in each block unit, and the inspection result in each block unit is put together to make a final synthesis. A determination is made.

  In order to perform the above-described inspection, the measurement object is set on the rotary table 13 (# 01). When a measurement start button (not shown) is operated (# 02 Yes branch), measurement is started. First, the laser slit projector 20 is turned on by the light source controller 80, and the slit light is irradiated (# 03). The X stage 14, the Y stage 15, and the rotary table 13 are operated so that the left edge of the first measurement block, which is the measurement start point, is irradiated with the slit light (# 04).

  X-axis direction scanning is performed while moving the X stage 14 at a constant speed in the positive direction (# 05). At the same time, the image data from the imaging unit 3 is transferred to the image memory 81 (# 06). This X-axis direction scanning and image data acquisition are performed until the slit light reaches the side edge of the measurement object. When the slit light reaches the side edge of the measurement object (# 07 Yes), scanning in the X-axis direction is stopped (# 08).

  When the scanning in the X-axis direction is stopped, the image processing unit 82 processes the transferred image data and generates the optical cutting line pixel position information (# 09). From this light section line pixel position information, the three-dimensional measurement data calculation unit 83 uses a table storing the relationship between the pixel position and the three-dimensional position calculated from the pixel position based on the triangulation method. The three-dimensional coordinate value of the deep groove is read based on the cutting line pixel position information, and this value is transferred as measurement point data to the memory address associated with each measurement point (# 10). Of course, instead of using the table, the calculation based on the triangulation method may be performed using the light cutting line pixel position information each time, and the three-dimensional coordinate value of the deep groove may be obtained as the measurement point data.

Subsequently, the shape evaluation module 90A executes an alignment routine for aligning measurement point data and reference point data using the above-described alignment algorithm (# 11). In this alignment routine, as shown in FIG. 8, measurement point data (position data of the measurement point group) is read (# 110), and reference point data corresponding to the measurement point group (position of the reference point group) Data) is read out (# 111). The measurement point and the reference point are associated with each other so as to have the smallest corresponding point distance (# 112). The above-mentioned distance weight coefficient between corresponding points: ρ _i and the distance weight coefficient between adjacent points: γ _i are calculated and multiplied to obtain a total weight coefficient: w _i (# 113). Further, using this total weight coefficient: w _i and the distance between corresponding points e _i , a successive convergence evaluation value: J is calculated (# 114). Based on the calculated successive convergence evaluation value, it is checked whether or not the associated corresponding point group is successively converged (# 115). If not converged (# 115 No branch), the process returns to step # 112 to associate the measurement point with the reference point again. In the case of convergence (# 115 Yes branch), a joint transformation parameter is generated so that the corresponding point group matches as much as possible (# 116). The position coordinate of the measurement point group is converted using the generated joint conversion parameter, and the measurement point group is moved (# 117). The average corresponding distance is calculated from the measurement point group after movement for calculating the average movement amount from the position coordinates after the movement of the measurement point group and before the movement and the reference point group (# 118). The calculated average corresponding distance average moving amount is compared with a preset threshold value to check whether the end condition is satisfied (# 119). In this check step, it is convenient to additionally set the upper limit number of repetitions. If the end condition is not satisfied (# 119 No branch), the process returns to step # 112 to associate the measurement point after movement with the reference point. When the termination condition is satisfied (# 119 Yes branch), this alignment routine is terminated.

When the alignment routine ends, the defect evaluation module 90B executes a defect determination routine for detecting a surface defect area using the above-described surface defect determination algorithm (# 12). In this defect determination routine, as shown in FIG. 9, first, using a first threshold that is a distance threshold between corresponding points, a set of measurement points (specific measurement) where the distance between corresponding points: ei is equal to or greater than the first threshold. A process of extracting (point group) as an erroneous correspondence region is executed (# 120). If no erroneous correspondence region is extracted (# 121 No branch), this defect determination routine is terminated. When an erroneous correspondence region is extracted (# 121 Yes branch), the measurement is such that the distance between adjacent points between measurement points included in the erroneous correspondence region is less than a second threshold value as a preset distance threshold between adjacent points. The point is specified as a nearby measurement point (# 122). Further, the number of specified nearby measurement points is counted (# 123). If the count number of the nearby measurement points is less than the third threshold value as a preset neighborhood score threshold (# 124 No branch), jump to step # 120, assuming that this miscorresponding region cannot be determined as a surface defect region, The next erroneous correspondence area is extracted. When the count number of the nearby measurement points is equal to or greater than the third threshold (# 124 Yes branch), this miscorresponding region is determined as a surface defect region (# 125), and the three-dimensional position coordinate values of the measuring points included in this miscorresponding region Are recorded as information on the surface defect area (# 126).

  When scanning in the X-axis direction is stopped in step # 08, the Y stage 15 is operated while the alignment routine and defect determination routine are being executed, and a shift in the Y-axis direction is performed at a predetermined pitch (# 13). That is, the evaluation process from steps # 09 to # 12 and the shift process in the Y-axis direction are performed simultaneously. When both the evaluation process and the shift process are completed, it is checked whether or not the X-axis direction scan still remains (# 14).

  If the X-axis direction scan still remains in the check of step # 14 (# 14 Yes branch), the direction of the X-axis direction scan is reversed (# 15), and the process returns to step # 05 to perform the X-axis direction scan. If no X-axis direction scanning remains in the check of step # 14 (# 14 No branch), the laser slit projector 20 is turned off and the slit light irradiation is stopped (# 16). Further, it is checked whether or not it is necessary to rotate the turntable 13 by 90 degrees in order to complement the measurement data of the non-measurable part accompanying the generation of the 90-degree rotation blind spot (# 17). If it is necessary to rotate the rotary table 13 by 90 degrees (# 17 Yes branch), the rotary table 13 is rotated by 90 degrees, and the process returns to step # 03 again to repeat this measurement. If the additional rotation of 90 degrees is not sufficient, another 90 rotations (180 degree position and 270 degree position with respect to the initial posture position) are performed every 90 degrees. When it is not necessary to rotate the turntable 13 by 90 degrees (# 17 No branch), comprehensive determination is performed based on the defect evaluation results in all measurement blocks (# 17). In this comprehensive determination, a defect position display diagram in which the position of the defect is marked on the overall view showing the entire measurement object can be output through a monitor or a print.

Hereinafter, another embodiment is illustrated.
(1) In the above embodiment, the distance weight coefficient between adjacent points is calculated in the alignment routine. However, when the distance weight coefficient between adjacent points is obtained based on the distance between adjacent points of the reference point group, it is calculated in advance. The calculation speed is increased by making a table in advance.
(2) In the above embodiment, many threshold values set in advance are used as determination conditions. However, the threshold values are not set in advance, and the surface shape of the measurement object or the statistical result of the measurement result A configuration in which the threshold value is calculated from various characteristics may be adopted.

Illustration of alignment algorithm used in the present invention Explanatory diagram of surface defect judgment algorithm used in the present invention The schematic diagram which shows typically the structure of the surface inspection apparatus which employ | adopted the positioning algorithm by this invention Illustrated perspective view showing configuration of measuring head of surface inspection apparatus Development view schematically showing the relationship between the slit optical axis and the imaging optical axis and the relationship between the imaging optical axis and the imaging surface in the measurement head. Functional block diagram showing the configuration of the evaluation module of the surface inspection device Flow chart showing flow of surface defect detection control in surface inspection apparatus Flow chart showing the alignment routine Flow chart showing defect determination routine

Explanation of symbols

1: Measuring device unit 2: Slit light source unit 3: Imaging unit 20: Laser slit projector 21: Cylindrical lens 30: Telecentric lens unit 31: Imaging unit 31a: Imaging surface 32: P polarizing plate 83: Evaluation unit 90: Evaluation module 90A : Shape evaluation module 90B: defect evaluation module 91: data input unit 92: point group association unit 93: reference point data storage unit 94: weight calculation unit 95: convergence evaluation unit 96: point group conversion unit 97: erroneous correspondence measurement group Extraction unit 98: Defect determination unit 100: Controller

Claims (6)

  A measurement data input unit for inputting measurement point data including position information of a large number of measurement points corresponding to the shape of the measurement object, and a reference point including position information of a large number of reference points corresponding to the reference shape of the measurement object A reference data storage unit for storing data, and an alignment processing means for aligning the measurement point and the reference point based on a sequential convergence process for sequentially converging the distance between the corresponding measurement point and the reference point; Shape evaluation means for evaluating the shape of the measurement object based on the measurement point data and the reference point data after the alignment processing by the alignment processing means, and the alignment processing means is adjacent to the measurement A weight coefficient determining means for determining a distance weight coefficient between adjacent points based on a distance between adjacent points between adjacent points or a distance between adjacent points between adjacent reference points; Object shape evaluation apparatus used to obtain the sequential convergence evaluation value in the sequential convergence process.   The weighting factor determining means determines a distance weighting factor between corresponding points based on a distance between corresponding points between the corresponding measurement point and a reference point, and the positioning processing means determines the distance weighting factor between corresponding points. The object shape evaluation apparatus according to claim 1, which is used when obtaining a sequential convergence evaluation value in the sequential convergence processing.   The object shape evaluation apparatus according to claim 1, wherein the distance weight coefficient between adjacent points is determined based on a distance between adjacent points of the reference point.   The object shape evaluation apparatus according to claim 3, wherein the distance weight coefficient between adjacent points is calculated in advance and tabulated.   5. The object shape evaluation apparatus according to claim 1, wherein the distance weight coefficient between adjacent points is obtained by a threshold function using the distance between adjacent points as a parameter. The measuring point data from claim 1 is a three-dimensional position data retrieved from the acquired distance image by processing the captured image of the measurement object illuminated by the slit light in any one of 5 The object shape evaluation apparatus described.

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